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experience in data acquisition and analysis of advance microscopy imaging techniques (e.g. confocal microscopy, TIRF, FRET, STORM, DNA-PAINT) is desired. WE OFFER: Career development in a multidisciplinary and
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should have demonstrated in-dependent experience in designing and carrying-out ethnographic fieldwork, and in qualitative data analysis. - Experience with participatory design or participatory
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The CRITICAL AGENCY project (funded by the DFG Emmy Noether programme) explores critique as it is manifest in critical encounters: critique as shaped by two logics – the logic of analysis and the
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Max Planck Institute of Biophysics, Frankfurt am Main | Frankfurt am Main, Hessen | Germany | 2 months ago
analysis of SMC motor proteins (the candidate with prior experience in quantitative (imaging) data analysis is preferred. Prior experience in microscopy is desired but not required.) Development of 3D
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ability to work in an interdisciplinary manner Creativity and a strong spirit of discovery to develop new research approaches Experience in planning and conducting experimental studies Very good data
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reporting skills (4) Experience in spatial data analysis using geographic information systems (GIS) and programming languages (R, Python) as well as experience in numerical model applications and multivariate
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Your Job: We are looking for a researcher to develop and apply machine learning models for genomic data in our lab. We focus on sequence analysis, genomics, semantics, and cross-domain data
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on two core but complementary areas: Computer vision and sensor data analysis, applied to tasks such as object detection in drone images (e.g., pest or disease detection), object tracking (e.g. leaves
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properties (IOPs/AOPs) for the German Baltic Sea coastal region. This will serve as a training data set for AI-driven simulations in the SEAGUARD project and contribute to the analysis of drivers of change in
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The Network Analysis and Modelling group investigates how genetic variation shapes gene regulation, protein function, and, ultimately, observable plant traits. Using machine learning and network